What is Contrastive Divergence?
Contrastive Divergence (CD) is a learning algorithm used primarily for training energy-based models such as Restricted Boltzmann Machines (RBMs) and certain types of Markov Random Fields. Developed by Geoffrey Hinton and his colleagues, CD is an approximation technique that speeds up the training of these models, which are otherwise computationally expensive due to the intractability of their partition functions.
Understanding Energy-Based Models
Before diving into Contrastive Divergence, it's important to understand the context in which it is used. Energy-based models (EBMs) like RBMs are a class of probabilistic graphical models that assign a scalar energy to each configuration of the variables they represent. The probability of a configuration is then defined in terms of this energy, with lower-energy configurations being more probable. However, computing the overall probability distribution requires summing over all possible configurations, which is often infeasible for large systems due to the exponential number of terms involved.
The Role of Contrastive Divergence
Contrastive Divergence addresses the computational challenge posed by the partition function in EBMs. The key insight behind CD is that it's possible to train these models without having to calculate the full partition function. Instead, CD focuses on adjusting the model parameters so that the probability of the observed data increases while the probability of samples generated by the model decreases.
To achieve this, CD performs a Gibbs sampling procedure starting from the training data to produce samples that the model believes are likely. It then uses these samples to estimate the gradient of the log-likelihood of the training data with respect to the model parameters. This gradient is used to update the parameters in a direction that improves the model's representation of the data.
Contrastive Divergence Algorithm
The CD algorithm can be summarized in the following steps:
- Start with a training example and compute the probabilities of the hidden units.
- Sample a hidden configuration based on these probabilities, yielding a "reconstruction" of the visible units.
- Update the model parameters based on the difference between the outer products of the original training example and the reconstructed sample.
The algorithm typically only runs the Gibbs sampler for a few steps (often just one), which is why the method is called Contrastive Divergence, as it contrasts the data-driven and model-driven updates. The "divergence" part of the name refers to the fact that the algorithm minimizes a divergence measure between the data distribution and the model distribution.
Advantages and Limitations
The primary advantage of Contrastive Divergence is its efficiency. By avoiding the direct computation of the partition function, CD allows for much faster training of EBMs, making them practical for real-world applications. However, CD is an approximation method, and the quality of the approximation depends on the number of Gibbs sampling steps taken. In practice, CD can lead to biased estimates of the model parameters, and the resulting models may not perfectly represent the probability distribution of the data.
Applications
Contrastive Divergence has been used successfully in various domains, particularly in unsupervised learning tasks where the goal is to learn a good representation of the input data. Applications include dimensionality reduction, feature learning, and as a pre-training step for deeper neural networks. RBMs trained with CD have been used in collaborative filtering, classification tasks, and even in the initial layers of deep learning architectures for image and speech recognition.
Conclusion
Contrastive Divergence is a powerful algorithm that has facilitated the training of complex probabilistic models like RBMs. By providing a practical way to train these models, CD has contributed to the advancement of unsupervised learning and deep learning. While it is not without its limitations, the efficiency gains offered by CD make it an important tool in the machine learning toolbox, particularly when dealing with large datasets and complex models.
